ILC-Unet++ for Covid-19 Infection Segmentation

Archive ouverte : Communication dans un congrès

Bougourzi, Fares | Distante, Cosimo | Dornaika, Fadi | Taleb-Ahmed, Abdelmalik | Hadid, Abdenour

Edité par HAL CCSD ; Springer International Publishing

International audience. Since the appearance of Covid-19 pandemic, in the end of 2019, Medical Imaging has been widely used to analysis this disease. In fact, CT-scans of the Lung can help to diagnosis, detect and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. In more details, we propose a CNN-based segmentation architecture named ILC-Unet++. The proposed ILC-Unet++ architecture, which is trained for both Covid-19 Infection and Lung Segmentation. The proposed architecture were tested using three datasets with two scenarios (intra and cross datasets). The experimental results showed that the proposed architecture performs better than three baseline segmentation architectures (Unet, Unet++ and Attention-Unet) and two Covid-19 infection segmentation architectures (SCOATNet and nCoVSegNet).

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